This section is intended to provide a background or context to the invention that is recited in the claims. The description herein may include concepts that could be pursued, but are not necessarily ones that have been previously conceived, implemented or described. Therefore, unless otherwise indicated herein, what is described in this section is not prior art to the description and claims in this application and is not admitted to be prior art by inclusion in this section.
Generally, stereo matching algorithm is a technique to create a disparity map by matching pixels from two images to estimate depth for objects in the scene. That is, the stereo matching algorithm is a technique to learn how far an object is distanced by matching of each pixel from a left image and a right image.
For example, in a case one finger is positioned between two eyes, and only left eye is opened, the finger is positioned only at a right side, and in a case only right eye is opened, the finger is positioned only at a left side. Meanwhile, in case of viewing a mountain peak at a far distance, the peak is positioned at a center regardless of a left eye being opened or a right eye being opened.
In a case an image is to be obtained using a stereo camera in the stereo matching algorithm, a position of an object captured by a camera is changed in response to a distance, and a depth image is extracted using this information.
What is important in designing this stereo camera is to align two sensors obtaining stereo image. A misalignment between the two sensors suffers from a disadvantage of generating a streaking noise in a depth image extracted from the stereo image.
FIG. 1a is an exemplary schematic view of a stereo image, and FIG. 1b is an exemplary schematic view of a depth image extracted from the stereo image of FIG. 1a. The stereo image (left image and right image) of FIG. 1a is a misaligned image, where a right image is misaligned relative to a left image to create a streaking noise in the depth image as shown in FIG. 1b. 
That is, a conventional stereo system suffers from disadvantages in that a streaking noise is generated in a depth image by tilt or rotation between sensors obtaining stereo image to decrease image recognition rate.